Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T17:54:02.977Z Has data issue: false hasContentIssue false
  • English
  • Français

The AusBeef model for beef production: II. sensitivity analysis

Published online by Cambridge University Press:  03 August 2017

H. C. DOUGHERTY ,
E. KEBREAB ,
M. EVERED ,
B. A. LITTLE ,
A. B. INGHAM ,
J. V. NOLAN ,
R. S. HEGARTY ,
D. PACHECO  and
M. J. MCPHEE
H. C. DOUGHERTY
Affiliation:
Department of Animal Science, University of California, Davis, CA 95616, USA
E. KEBREAB
Affiliation:
Department of Animal Science, University of California, Davis, CA 95616, USA
M. EVERED
Affiliation:
NSW DPI, Beef Industry Centre of Excellence, Trevenna Road N.S.W 2351, Armidale, Australia
B. A. LITTLE
Affiliation:
CSIRO Agriculture, St. Lucia, QLD 4067, Australia
A. B. INGHAM
Affiliation:
CSIRO Agriculture, St. Lucia, QLD 4067, Australia
J. V. NOLAN
Affiliation:
School of Env. Rural Science, University of New England, Armidale, NSW 2351, Australia
R. S. HEGARTY
Affiliation:
School of Env. Rural Science, University of New England, Armidale, NSW 2351, Australia
D. PACHECO
Affiliation:
AgResearch Grasslands, Palmerston North 4442, New Zealand
M. J. MCPHEE*
Affiliation:
NSW DPI, Beef Industry Centre of Excellence, Trevenna Road N.S.W 2351, Armidale, Australia
*
*To whom all correspondence should be addressed: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The present study evaluated the behaviour of the AusBeef model for beef production as part of a 2 × 2 study simulating performance on forage-based and concentrate-based diets from Oceania and North America for four methane (CH4)-relevant outputs of interest. Three sensitivity analysis methods, one local and two global, were conducted. Different patterns of sensitivity were observed between forage-based and concentrate-based diets, but patterns were consistent within diet types. For the local analysis, 36, 196, 47 and 8 out of 305 model parameters had normalized sensitivities of 0, >0, >0·01 and >0·1 across all diets and outputs, respectively. No parameters had a normalized local sensitivity >1 across all diets and outputs. However, daily CH4 production had the greatest number of parameters with normalized local sensitivities >1 for each individual diet. Parameters that were highly sensitive for global and local analyses across the range of diets and outputs examined included terms involved in microbial growth, volatile fatty acid (VFA) yields, maximum absorption rates and their inhibition due to pH effects and particle exit rates. Global sensitivity analysis I showed the high sensitivity of forage-based diets to lipid entering the rumen, which may be a result of the use of a feedlot-optimized model to represent high-forage diets and warrants further investigation. Global sensitivity analysis II showed that when all parameter values were simultaneously varied within ±10% of initial value, >96% of output values were within ±20% of the baseline, which decreased to >50% when parameter value boundaries were expanded to ±25% of their original values, giving a range for robustness of model outputs with regards to potential different ‘true’ parameter values. There were output-specific differences in sensitivity, where outputs that had greater maximum local sensitivities displayed greater degrees of non-linear interaction in global sensitivity analysis I and less variance in output values for global sensitivity analysis II. For outputs with less interaction, such as the acetate : propionate ratio and microbial protein production, the single most sensitive term in global sensitivity analysis I contributed more to the overall total-order sensitivity than for outputs with more interaction, with an average of 49, 33, 15 and 14% of total-order sensitivity for microbial protein production, acetate : propionate ratio, CH4 production and energy from absorbed VFAs, respectively. Future studies should include data collection for highly sensitive parameters reported in the present study to improve overall model accuracy.

Type
Modelling Animal Systems Research Papers
Information
The Journal of Agricultural Science , Volume 155 , Issue 9 , November 2017 , pp. 1459 - 1474
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baldwin, R. L. (1995). Modeling Ruminant Digestion and Metabolism. London, UK: Chapman & Hall.Google Scholar
Baldwin, R. L. & Black, J. L. (1979). Simulation of the Effects of Nutritional and Physiological Status on the Growth of Mammalian Tissues: Description and Evaluation of A Computer Program. Animal Research Laboratories Technical Paper 6. Melbourne, Australia: Commonwealth Science and Industrial Research Organization.Google Scholar
Baldwin, R. L., France, J. & Gill, M. (1987a). Metabolism of the lactating cow. I. Animal elements of a mechanistic model. Journal of Dairy Research 54, 77–105.CrossRefGoogle ScholarPubMed
Baldwin, R. L., Thornley, J. H. M. & Beever, D. E. (1987b). Metabolism of the lactating cow. II. Digestive elements of a mechanistic model. Journal of Dairy Research 54, 107–131.CrossRefGoogle ScholarPubMed
Baldwin, R. L., France, J., Beever, D. E., Gill, M. & Thornley, J. H. M. (1987c). Metabolism of the lactating cow. III. Properties of mechanistic models suitable for evaluation of energetic relationships and factors involved in the partition of nutrients. Journal of Dairy Research 54, 133–145.CrossRefGoogle ScholarPubMed
Bateman, H. G. II, Hanigan, M. D. & Kohn, R. A. (2008). Sensitivity of two metabolic models of dairy cattle digestion and metabolism to changes in nutrient content of diets. Animal Feed Science & Technology 140, 272–292.CrossRefGoogle Scholar
Boadi, D. A., Wittenberg, K. M. & McCaughey, W. P. (2002). Effects of grain supplementation on methane production of grazing steers using the sulphur hexafluoride (SF6) tracer gas technique. Canadian Journal of Animal Science 82, 151–157.CrossRefGoogle Scholar
Brown, M. S., Ponce, C. H. & Pulikanti, R. (2006). Adaptation of beef cattle to high-concentrate diets: performance and ruminal metabolism. Journal of Animal Science 84, E25–E33.CrossRefGoogle ScholarPubMed
Dewhurst, R. J., Davies, D. R. & Merry, R. J. (2000). Microbial protein supply from the rumen. Animal Feed Science and Technology 85, 1–21.CrossRefGoogle Scholar
Dijkstra, J., Neal, H. D., Beever, D. E. & France, J. (1992). Simulation of nutrient digestion, absorption and outflow in the rumen: model description. Journal of Nutrition 122, 2239–2256.CrossRefGoogle ScholarPubMed
Dougherty, H. C., Kebreab, E., Evered, M., Little, B. A., Ingham, A. B., Hegarty, R. S., Pacheco, D. & McPhee, M. J. (2017). The AusBeef model for beef production: I. Description and evaluation. Journal of Agricultural Science, Cambridge.Google Scholar
Eckard, R. J., Grainger, C. & De Klein, C. A. M. (2010). Options for the abatement of methane and nitrous oxide from ruminant production: a review. Livestock Science 130, 47–56.CrossRefGoogle Scholar
Ehle, F. R., Murphy, M. R. & Clark, J. H. (1982). In situ particle size reduction and the effect of particle size on degradation of crude protein and dry matter in the rumen of dairy steers. Journal of Dairy Science 65, 963–971.CrossRefGoogle Scholar
FAO (2015). Towards A Water and Food Secure Future. Critical Perspectives for Policy-Makers. Rome, Italy: FAO. Available online from: http://www.fao.org/3/a-i4560e.pdf (accessed 31 May 2017).Google Scholar
Gabel, G., Aschenbach, J. R. & Müller, F. (2002). Transfer of energy substrates across the ruminal epithelium: implications and limitations. Animal Health Research Reviews 3, 15–30.CrossRefGoogle ScholarPubMed
Gerber, P. J., Mottet, A., Opio, C. I., Falcucci, A. & Teillard, F. (2015). Environmental impacts of beef production: review of challenges and perspectives for durability. Meat Science 109, 2–12.CrossRefGoogle ScholarPubMed
Greenwood, P. L., Siddell, J. P., Walmsley, B. J., Geesink, G. H., Pethick, D. W. & McPhee, M. J. (2015). Postweaning substitution of grazed forage with a high-energy concentrate has variable long-term effects on subcutaneous fat and marbling in Bos taurus genotypes. Journal of Animal Science 93, 4132–4143.CrossRefGoogle ScholarPubMed
Gregorini, P., Beukes, P. C., Waghorn, G., Pacheco, D. & Hanigan, M. (2015). Development of an improved representation of rumen digesta outflow in a mechanistic and dynamic model of a dairy cow, Molly. Ecological Modelling 313, 293–306.CrossRefGoogle Scholar
Hanigan, M. D., Palliser, C. C. & Gregorini, P. (2009). Altering the representation of hormones and adding consideration of gestational metabolism in a metabolic cow model reduced prediction errors. Journal of Dairy Science 92, 5043–5056.CrossRefGoogle Scholar
Hanigan, M. D., Appuhamy, J. A. D. R. N. & Gregorini, P. (2013). Revised digestive parameter estimates for the Molly cow model. Journal of Dairy Science 96, 3867–3885.CrossRefGoogle ScholarPubMed
Hegarty, R. S. (2016). Impacts of CFI Methodologies on Whole-Farm Systems. Final Report. Filling the Research Gap Program. Canberra, Australia: Department of Agriculture, University of New England. Available online at: https://www.une.edu.au/__data/assets/pdf_file/0011/166808/Abridged_draft_Report_AusBeef.pdf (accessed 30 June 2017).Google Scholar
Herrmann, N. (2013). AusFarm – a Tutorial Version 1.8. Available online from: http://www.grazplan.csiro.au/files/AusFarm%20-%20a%20tutorial.pdf (accessed 27 June 2017).Google Scholar
Jonker, A., Muetzel, S., Molano, G. & Pacheco, D. (2016). Effect of fresh pasture forage quality, feeding level and supplementation on methane emissions from growing beef cattle. Animal Production Science 56, 1714–1721.CrossRefGoogle Scholar
Lascano, G. J. & Heinrichs, A. J. (2009). Rumen fermentation pattern of dairy heifers fed restricted amounts of low, medium and high concentrate diets with and without yeast culture. Livestock Science 124, 48–57.CrossRefGoogle Scholar
Leng, R. A. (1991). Application of Biotechnology to Nutrition of Animals in Developing Countries. Rome, Italy: FAO.Google Scholar
McAllister, T. A., Okine, E. K., Mathison, G. W. & Cheng, K.-J. (1996). Dietary, environmental and microbiological aspects of methane production in ruminants. Canadian Journal of Animal Science 76, 231–243.CrossRefGoogle Scholar
Mills, J. A. N., Dijkstra, J., Bannink, A., Cammell, S. B., Kebreab, E. & France, J (2001). A mechanistic model of whole-tract digestion and methanogenesis in the lactating dairy cow: model development, evaluation and application. Journal of Animal Science 79, 1584–1597.CrossRefGoogle ScholarPubMed
Nafikov, R. A. & Beitz, D. C. (2007). Carbohydrate and lipid metabolism in farm animals. The Journal of Nutrition 137, 702–705.CrossRefGoogle ScholarPubMed
Nagorcka, B. N. (2004a). AUSBEEF: a decision support system for cattle feedlots and the PGLP (Premium Grains for Livestock Program). Lethbridge, Canada: Canadian Beef Research Center Seminar, July 2004. https://publications.csiro.au/rpr/search?q=AUSBEEF%3A+a+decision+support+system+for+cattle+feedlots+and+the+PGLP+%28Premium+Grains+for+Livestock+Program%29 (accessed 27 June 2017).Google Scholar
Nagorcka, B. N. (2004b). A description of AUSBEEF ruminant model highlighting the differences with the current models CNCPS and MOLLY. Faculty of Animal Science, University of California Seminar, August, 2004, University of California. https://publications.csiro.au/rpr/search?q=A%20description%20of%20AUSBEEF%20ruminant%20model%20highlighting%20the%20differences%20with%20the%20current%20models%20CNCPS%20and%20MOLLY.&p=1&rpp=25&sb=RECENT (accessed 27 June 2017).Google Scholar
Nagorcka, B. N. & Zurcher, E. J. (2002). The potential gains achievable through access to more advanced/mechanistic models of ruminants. Animal Production in Australia: Proceedings of the Australian Society of Animal Production 24, 455–461.Google Scholar
Nagorcka, B. N., Gordon, G. L. R. & Dynes, R. A. (2000). Towards a more accurate representation of fermentation in mathematical models of the rumen. In Modelling Nutrient Utilization in Farm Animals (Eds McNamara, J. P., France, J. & Beever, D.), pp. 37–48. Wallingford, UK: CAB International.CrossRefGoogle Scholar
Newbold, C. J., De La Fuente, G., Belanche, A., Ramos-Morales, E. & McEwan, N. R. (2015). The role of ciliate protozoa in the rumen. Frontiers in Microbiology 6, 1313. doi: 10.3389/fmicb.2015.01313.CrossRefGoogle ScholarPubMed
Pujol, G., Iooss, B., Janon, A., Boumhaout, K., Da Veiga, S., Delage, T., Fruth, J., Gilquin, L., Guillaume, J., Le Gratiet, L., Lemaitre, P., Nelson, B. L., Monari, F., Oomen, R., Ramos, B., Roustant, O., Song, E., Staum, J., Touati, T. & Weber, F. (2016). Package ‘Sensitivity’: Global Sensitivity Analysis of Model Outputs. The R Project. Vienna, Austria: R Foundation for Statistical Computing. Available online from: https://cran.r-project.org/web/packages/sensitivity/sensitivity.pdf (accessed 23 April 2017).Google Scholar
R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available online from: https://www.R-project.org/ (accessed 23 April 2017).Google Scholar
Saltelli, A. & Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software 25, 1508–1517.CrossRefGoogle Scholar
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Chichester, UK: John Wiley and Sons, Ltd.Google Scholar
Sayre, N. F., Carlisle, L., Huntsinger, L., Fisher, G. & Shattuck, A. (2012). The role of rangelands in diversified farming systems: innovations, obstacles and opportunities in the USA. Ecology and Society 17, 43. http://dx.doi.org/10.5751/ES-04790-170443.CrossRefGoogle Scholar
Soetaert, K. & Petzoldt, T. (2015). A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability and Monte Carlo Analysis. The R Project. Vienna, Austria: R Foundation for Statistical Computing. Available online from: https://cran.r-project.org/web/packages/FME/FME.pdf (accessed 2 June 2017).Google Scholar
Thompson, V. A., Sainz, R. D., Strathe, A. B., Rumsey, T. R. & Fadel, J. G. (2014). The evaluation of a dynamic, mechanistic, thermal balance model for Bos indicus and Bos Taurus. Journal of Agricultural Science, Cambridge 152, 483–496.CrossRefGoogle Scholar
US EPA (United States Environmental Protection Agency) (2012). Global Anthropogenic non-CO2 Greenhouse Gas Emissions: 1990–2030. Washington, DC, USA: US EPA. Available online at: https://www.epa.gov/sites/production/files/2016-08/documents/epa_global_nonco2_projections_dec2012.pdf (accessed 31 May 2017).Google Scholar
Van Lingen, H. J., Plugge, C. M., Fadel, J. G., Kebreab, E., Bannink, A. & Dijkstra, J. (2016). Thermodynamic driving force of hydrogen on rumen microbial metabolism: a theoretical investigation. PLoS ONE 11, e0161362. https://doi.org/10.1371/journal.pone.0161362.Google ScholarPubMed
Veira, D. M. (1986). The role of ciliate protozoa in nutrition of the ruminant. Journal of Animal Science 63, 1547–1560.CrossRefGoogle ScholarPubMed